Muscle fatigue is the inability to exert the required force. Surface Electromyography (sEMG) is a technique used to study the muscle's electrical property. These generated signals are complex and nonstationary in nature. In this work, an attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition. The sEMG signals of 41 healthy adult volunteers are acquired from the biceps brachii muscle during isometric contraction with a 6 Kg load. The subjects are asked to perform the exercise until they are unable to continue. The signals are preprocessed, and the first and last 500 ms of the signal are considered for analysis. The segmented signals are subjected to sequential visibility graph algorithm. Further, the number of motifs for a subgraph of four is calculated. The results show that the signals are unique for each subject. The frequency of higher degree motif is more in the case of fatigue. The frequency of each unique motif is capable of differentiating nonfatigue and fatigue conditions. Nonparametric statistical test result indicates all features are significant with p<0.05. This method of analysis can be extended to other varied neuromuscular conditions. © 2018 IEEE.